mindspore.ops.pow
- mindspore.ops.pow(input, exponent)[source]
Calculates the exponent power of each element in input.
\[out_{i} = input_{i} ^{ exponent_{i}}\]Note
Inputs of input and exponent comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one scalar.
When the inputs are two tensors, dtypes of them cannot be bool at the same time, and the shapes of them can be broadcast.
- Parameters
input (Union[Tensor, number.Number, bool]) – The first input is a number.Number or a bool or a tensor whose data type is number or bool_.
exponent (Union[Tensor, number.Number, bool]) – The second input, when the first input is a Tensor, the second input should be a number.Number or bool value, or a Tensor whose data type is number or bool_. When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.
- Returns
Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.
- Raises
TypeError – If input and exponent is not one of the following: Tensor, number.Number or bool.
ValueError – If the shape of input and exponent are different.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) >>> y = 3.0 >>> output = ops.pow(x, y) >>> print(output) [ 1. 8. 64.] >>> >>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) >>> y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32) >>> output = ops.pow(x, y) >>> print(output) [ 1. 16. 64.]